Video denoising technology is very important in video quality improvement. For example, in low light conditions, video capture can introduce noise. Denoising uses temporal and spatial frame information to reduce noise in video frames. Temporal frame information relates to data between frames, and spatial frame information relates to information within a frame.
Existing denoising techniques typically rely on either temporal or spatial filtering. In temporal filtering, adjacent frames are compared to determine the noise (incorrect data) in the frame. While useful, temporal filtering can introduce ghost and floating artifacts, which can be annoying to the viewer. Spatial filtering avoids the ghost and floating artifacts of temporal filtering, but changes the characteristic of the noise to more of a salt and pepper type and can blur low level details.
Another form of denoising is a non-local mean based denoising algorithm. But this form of denoising is computationally very intensive, and therefore not as useful in situations where the computational ability is limited.
A need remains for a way to improve denoising of video data, that addresses these and other problems associated with the prior art.